How do people learn, revise, and reason about everyday disease, biological, physics, artifact, and social categories? This proposal describes 5 detailed experimental plans to study the nature and the effects of statistical and causal information on category learning and inference. The presence and structure of explanatory, causal knowledge about the interrelations among the properties associated with novel, but plausible everyday categories would be manipulated. At the same time, in a factorial design, the statistical structure of the properties associated with members of the categories would be manipulated. The effects of explanatory and statistical information on learning, classification, inductive inferences, and similarity judgments will be measured. Later experiments would study the revision of beliefs about the nature of category attributes in the presence or absence of background knowledge relevant to the attributes. The results will advance our theoretical and practical knowledge about category representation and contribute to education and to the effective communication of health-relevant information.